Arbeitspapier
Learning machines supporting bankruptcy prediction
In many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification rule called a score or an SVM. Companies with scores above zero belong to one group and the rest to another group. Estimation of the probability of default (PD) values can be calculated from the scores provided by an SVM. The transformation used in this paper is a combination of weighting ranks and of smoothing the results using the PAV algorithm. The conversion is then monotone. This discussion paper is based on the Creditreform database from 1997 to 2002. The indicator variables were converted to financial ratios; it transpired out that eight of the 25 were useful for the training of the SVM. The results showed that those ratios belong to activity, profitability, liquidity and leverage. Finally, we conclude that SVMs are capable of extracting the necessary information from financial balance sheets and then to predict the future solvency or insolvent of a company. Banks in particular will benefit from these results by allowing them to be more aware of their risk when lending money.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 649 Discussion Paper ; No. 2010,032
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Bankruptcy; Liquidation
Neural Networks and Related Topics
- Thema
-
Support Vector Machine
Bankruptcy
Default Probabilities Prediction
Profitability
Kreditwürdigkeit
Konkurs
Prognoseverfahren
Support Vector Machine
Schätzung
Deutschland
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Härdle, Wolfgang Karl
Moro, Rouslan A.
Hoffmann, Linda
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (wo)
-
Berlin
- (wann)
-
2010
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:46 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Härdle, Wolfgang Karl
- Moro, Rouslan A.
- Hoffmann, Linda
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Entstanden
- 2010
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